COMPARISON RESULTS OF MULTI-CRITERIA ANALYSIS METHODS: SAW, TOPSIS, AND MAUT IN CONTRACTOR SELECTION AT PT X

  • William Program Studi Teknik Sipil, Fakultas Teknik, Universitas Tarumanagara
  • Fuk Jin Oei Program Studi Teknik Sipil, Fakultas Teknik, Universitas Tarumanagara
DOI: https://doi.org/10.35814/infrastruktur.v10i2.6936
Abstract views: 25 | pdf downloads: 16
Keywords: Simple Additive Weighting (SAW), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Multi-Attribute Utility Theory (MAUT), Multi-Criteria Analysis (MCA), contractor selection

Abstract

PT X, a company operating in the Fast Moving Consumer Goods (FMCG) sector located in East Jakarta, is expanding its Distribution Center (DC) to help increase distribution rates. Selecting the right contractor can help expedite the supply chain and shape the company's strategy. PT X faces several issues related to contractor selection with various criteria in the DC construction process. To address these issues, careful contractor selection is necessary. Multi-Criteria Analysis (MCA) methods such as Simple Additive Weighting (SAW), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and Multi-Attribute Utility Theory (MAUT) are used in the contractor selection system. The SAW and MAUT methods have advantages in simple and easy calculations, while TOPSIS excels in detailed data processing. This study aims to compare these three methods in contractor selection for the DC construction tender. The research data were obtained through questionnaires and interviews with stakeholders and PT X management. The analysis results show that, based on Euclidean Distance (ED) and Average Error Rate (AER), the MAUT method is considered more suitable for application at PT X, with an ED value of 0.4012 and an AER of 0%.

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Published
2024-10-26
How to Cite
William, & Oei, F. J. (2024). COMPARISON RESULTS OF MULTI-CRITERIA ANALYSIS METHODS: SAW, TOPSIS, AND MAUT IN CONTRACTOR SELECTION AT PT X. Jurnal Infrastruktur , 10(2), 105 - 112. https://doi.org/10.35814/infrastruktur.v10i2.6936